Overview

Dataset statistics

Number of variables29
Number of observations1000
Missing cells1596
Missing cells (%)5.5%
Total size in memory4.3 MiB
Average record size in memory4.4 KiB

Variable types

Numeric1
Text28

Alerts

lobbyist_is_colobbyist has constant value ""Constant
lobbyist_has_finacial_interest has constant value ""Constant
num_periods has constant value ""Constant
reimbursed_expenses_total has constant value ""Constant
lobbyist_targets has 13 (1.3%) missing valuesMissing
periodic_id has 67 (6.7%) missing valuesMissing
registration_id has 67 (6.7%) missing valuesMissing
period has 67 (6.7%) missing valuesMissing
periodic_year has 67 (6.7%) missing valuesMissing
compensation_total has 67 (6.7%) missing valuesMissing
lobbying_expenses_total has 67 (6.7%) missing valuesMissing
small_expense_total has 67 (6.7%) missing valuesMissing
itemized_expense_total has 67 (6.7%) missing valuesMissing
salary_expense_total has 67 (6.7%) missing valuesMissing
reimbursed_expenses_total has 67 (6.7%) missing valuesMissing
periodic_activities has 456 (45.6%) missing valuesMissing
periodic_targets has 457 (45.7%) missing valuesMissing
0 has unique valuesUnique

Reproduction

Analysis started2023-12-09 21:04:16.106006
Analysis finished2023-12-09 21:04:17.485371
Duration1.38 second
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

0
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T21:04:17.610415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.8194361
Coefficient of variation (CV)0.5770618104
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.66667
MonotonicityStrictly increasing
2023-12-09T21:04:17.768711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
Distinct238
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
2023-12-09T21:04:18.218900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)6.8%

Sample

1st row518646
2nd row512122
3rd row509200
4th row515387
5th row508540
ValueCountFrequency (%)
373217 6
 
0.6%
457282 6
 
0.6%
389122 6
 
0.6%
501862 6
 
0.6%
339411 6
 
0.6%
498125 6
 
0.6%
363313 6
 
0.6%
447605 6
 
0.6%
389109 6
 
0.6%
401147 6
 
0.6%
Other values (228) 940
94.0%
2023-12-09T21:04:18.791887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1345
22.4%
4 706
11.8%
9 622
10.4%
1 558
9.3%
6 530
 
8.8%
2 473
 
7.9%
5 463
 
7.7%
0 435
 
7.2%
7 434
 
7.2%
8 434
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1345
22.4%
4 706
11.8%
9 622
10.4%
1 558
9.3%
6 530
 
8.8%
2 473
 
7.9%
5 463
 
7.7%
0 435
 
7.2%
7 434
 
7.2%
8 434
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 6000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1345
22.4%
4 706
11.8%
9 622
10.4%
1 558
9.3%
6 530
 
8.8%
2 473
 
7.9%
5 463
 
7.7%
0 435
 
7.2%
7 434
 
7.2%
8 434
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1345
22.4%
4 706
11.8%
9 622
10.4%
1 558
9.3%
6 530
 
8.8%
2 473
 
7.9%
5 463
 
7.7%
0 435
 
7.2%
7 434
 
7.2%
8 434
 
7.2%
Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size60.5 KiB
2023-12-09T21:04:18.997926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.773
Min length3

Characters and Unicode

Total characters4773
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row969
2nd row969
3rd row969
4th row969
5th row969
ValueCountFrequency (%)
31921 393
39.3%
86741 109
 
10.9%
86092 79
 
7.9%
969 66
 
6.6%
361 31
 
3.1%
73976 31
 
3.1%
80558 31
 
3.1%
83583 29
 
2.9%
86285 28
 
2.8%
37489 25
 
2.5%
Other values (16) 178
17.8%
2023-12-09T21:04:19.330067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1043
21.9%
9 715
15.0%
3 597
12.5%
2 577
12.1%
8 527
11.0%
6 383
 
8.0%
7 344
 
7.2%
5 230
 
4.8%
4 186
 
3.9%
0 171
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4773
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1043
21.9%
9 715
15.0%
3 597
12.5%
2 577
12.1%
8 527
11.0%
6 383
 
8.0%
7 344
 
7.2%
5 230
 
4.8%
4 186
 
3.9%
0 171
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1043
21.9%
9 715
15.0%
3 597
12.5%
2 577
12.1%
8 527
11.0%
6 383
 
8.0%
7 344
 
7.2%
5 230
 
4.8%
4 186
 
3.9%
0 171
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1043
21.9%
9 715
15.0%
3 597
12.5%
2 577
12.1%
8 527
11.0%
6 383
 
8.0%
7 344
 
7.2%
5 230
 
4.8%
4 186
 
3.9%
0 171
 
3.6%
Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size86.7 KiB
2023-12-09T21:04:19.634767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length67
Median length59
Mean length31.695
Min length16

Characters and Unicode

Total characters31695
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreenberg Traurig, LLP
2nd rowGreenberg Traurig, LLP
3rd rowGreenberg Traurig, LLP
4th rowGreenberg Traurig, LLP
5th rowGreenberg Traurig, LLP
ValueCountFrequency (%)
llc 596
 
13.7%
cozen 393
 
9.0%
public 393
 
9.0%
strategies 393
 
9.0%
o'connor 393
 
9.0%
alpha 109
 
2.5%
strategic 109
 
2.5%
planning 109
 
2.5%
corp 109
 
2.5%
llp 97
 
2.2%
Other values (66) 1657
38.0%
2023-12-09T21:04:20.071739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3358
 
10.6%
n 2426
 
7.7%
e 2193
 
6.9%
C 1977
 
6.2%
o 1846
 
5.8%
r 1843
 
5.8%
i 1673
 
5.3%
t 1513
 
4.8%
L 1481
 
4.7%
a 1418
 
4.5%
Other values (39) 11967
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19521
61.6%
Uppercase Letter 7420
 
23.4%
Space Separator 3358
 
10.6%
Other Punctuation 1396
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2426
12.4%
e 2193
11.2%
o 1846
9.5%
r 1843
9.4%
i 1673
8.6%
t 1513
 
7.8%
a 1418
 
7.3%
l 962
 
4.9%
g 945
 
4.8%
s 903
 
4.6%
Other values (13) 3799
19.5%
Uppercase Letter
ValueCountFrequency (%)
C 1977
26.6%
L 1481
20.0%
S 874
11.8%
P 684
 
9.2%
A 640
 
8.6%
O 465
 
6.3%
I 250
 
3.4%
E 174
 
2.3%
T 151
 
2.0%
D 132
 
1.8%
Other values (11) 592
 
8.0%
Other Punctuation
ValueCountFrequency (%)
, 646
46.3%
' 417
29.9%
. 302
21.6%
& 31
 
2.2%
Space Separator
ValueCountFrequency (%)
3358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26941
85.0%
Common 4754
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2426
 
9.0%
e 2193
 
8.1%
C 1977
 
7.3%
o 1846
 
6.9%
r 1843
 
6.8%
i 1673
 
6.2%
t 1513
 
5.6%
L 1481
 
5.5%
a 1418
 
5.3%
l 962
 
3.6%
Other values (34) 9609
35.7%
Common
ValueCountFrequency (%)
3358
70.6%
, 646
 
13.6%
' 417
 
8.8%
. 302
 
6.4%
& 31
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3358
 
10.6%
n 2426
 
7.7%
e 2193
 
6.9%
C 1977
 
6.2%
o 1846
 
5.8%
r 1843
 
5.8%
i 1673
 
5.3%
t 1513
 
4.8%
L 1481
 
4.7%
a 1418
 
4.5%
Other values (39) 11967
37.8%
Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size70.8 KiB
2023-12-09T21:04:20.367618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length20
Median length19
Mean length15.395
Min length9

Characters and Unicode

Total characters15395
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohn Mascialino
2nd rowJohn Mascialino
3rd rowJohn Mascialino
4th rowJohn Mascialino
5th rowJohn Mascialino
ValueCountFrequency (%)
stuart 393
19.5%
shorenstein 393
19.5%
richard 109
 
5.4%
lipsky 109
 
5.4%
amelia 81
 
4.0%
adams 81
 
4.0%
john 66
 
3.3%
mascialino 66
 
3.3%
rodney 31
 
1.5%
oglesby 31
 
1.5%
Other values (51) 651
32.4%
2023-12-09T21:04:20.783131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1393
 
9.0%
e 1312
 
8.5%
t 1238
 
8.0%
a 1237
 
8.0%
r 1212
 
7.9%
i 1058
 
6.9%
1011
 
6.6%
s 903
 
5.9%
S 789
 
5.1%
h 717
 
4.7%
Other values (37) 4525
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12356
80.3%
Uppercase Letter 2014
 
13.1%
Space Separator 1011
 
6.6%
Other Punctuation 11
 
0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1393
11.3%
e 1312
10.6%
t 1238
10.0%
a 1237
10.0%
r 1212
9.8%
i 1058
8.6%
s 903
7.3%
h 717
 
5.8%
o 688
 
5.6%
u 598
 
4.8%
Other values (15) 2000
16.2%
Uppercase Letter
ValueCountFrequency (%)
S 789
39.2%
R 188
 
9.3%
A 180
 
8.9%
L 167
 
8.3%
M 144
 
7.1%
B 97
 
4.8%
J 85
 
4.2%
H 66
 
3.3%
C 50
 
2.5%
T 49
 
2.4%
Other values (9) 199
 
9.9%
Space Separator
ValueCountFrequency (%)
1011
100.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14370
93.3%
Common 1025
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1393
 
9.7%
e 1312
 
9.1%
t 1238
 
8.6%
a 1237
 
8.6%
r 1212
 
8.4%
i 1058
 
7.4%
s 903
 
6.3%
S 789
 
5.5%
h 717
 
5.0%
o 688
 
4.8%
Other values (34) 3823
26.6%
Common
ValueCountFrequency (%)
1011
98.6%
. 11
 
1.1%
- 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1393
 
9.0%
e 1312
 
8.5%
t 1238
 
8.0%
a 1237
 
8.0%
r 1212
 
7.9%
i 1058
 
6.9%
1011
 
6.6%
s 903
 
5.9%
S 789
 
5.1%
h 717
 
4.7%
Other values (37) 4525
29.4%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
2023-12-09T21:04:20.948573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023
ValueCountFrequency (%)
2018 346
34.6%
2019 272
27.2%
2020 124
 
12.4%
2022 107
 
10.7%
2021 84
 
8.4%
2023 67
 
6.7%
2023-12-09T21:04:21.221667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1489
37.2%
0 1124
28.1%
1 702
17.5%
8 346
 
8.6%
9 272
 
6.8%
3 67
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1489
37.2%
0 1124
28.1%
1 702
17.5%
8 346
 
8.6%
9 272
 
6.8%
3 67
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1489
37.2%
0 1124
28.1%
1 702
17.5%
8 346
 
8.6%
9 272
 
6.8%
3 67
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1489
37.2%
0 1124
28.1%
1 702
17.5%
8 346
 
8.6%
9 272
 
6.8%
3 67
 
1.7%
Distinct150
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size84.9 KiB
2023-12-09T21:04:21.588873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length99
Median length53
Mean length29.836
Min length4

Characters and Unicode

Total characters29836
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)5.4%

Sample

1st rowNational Strategies, LLC for the benefit of Tanium Inc.
2nd rowNetApp Inc
3rd rowNew York State Messenger & Courier Association, Inc.
4th rowNews Corporation
5th rowNYU Langone Hospitals on behalf of NYU Langone Hospitals and Sunset Park Health Council, Inc.
ValueCountFrequency (%)
inc 436
 
10.1%
llc 228
 
5.3%
the 156
 
3.6%
of 114
 
2.6%
association 96
 
2.2%
management 83
 
1.9%
york 73
 
1.7%
services 73
 
1.7%
new 68
 
1.6%
corporation 54
 
1.3%
Other values (312) 2921
67.9%
2023-12-09T21:04:22.131021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3302
 
11.1%
e 2258
 
7.6%
n 1990
 
6.7%
o 1761
 
5.9%
t 1567
 
5.3%
r 1493
 
5.0%
a 1445
 
4.8%
i 1429
 
4.8%
s 979
 
3.3%
C 969
 
3.2%
Other values (58) 12643
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18857
63.2%
Uppercase Letter 6406
 
21.5%
Space Separator 3302
 
11.1%
Other Punctuation 1162
 
3.9%
Decimal Number 65
 
0.2%
Dash Punctuation 30
 
0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2258
12.0%
n 1990
10.6%
o 1761
9.3%
t 1567
 
8.3%
r 1493
 
7.9%
a 1445
 
7.7%
i 1429
 
7.6%
s 979
 
5.2%
l 933
 
4.9%
c 892
 
4.7%
Other values (15) 4110
21.8%
Uppercase Letter
ValueCountFrequency (%)
C 969
15.1%
A 697
10.9%
I 678
10.6%
L 664
10.4%
S 580
 
9.1%
T 372
 
5.8%
M 275
 
4.3%
E 264
 
4.1%
N 248
 
3.9%
R 195
 
3.0%
Other values (15) 1464
22.9%
Decimal Number
ValueCountFrequency (%)
1 19
29.2%
5 16
24.6%
2 8
12.3%
8 7
 
10.8%
0 6
 
9.2%
6 6
 
9.2%
7 3
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 568
48.9%
, 491
42.3%
' 49
 
4.2%
& 35
 
3.0%
/ 19
 
1.6%
Close Punctuation
ValueCountFrequency (%)
] 6
85.7%
) 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
[ 6
85.7%
( 1
 
14.3%
Space Separator
ValueCountFrequency (%)
3302
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25263
84.7%
Common 4573
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2258
 
8.9%
n 1990
 
7.9%
o 1761
 
7.0%
t 1567
 
6.2%
r 1493
 
5.9%
a 1445
 
5.7%
i 1429
 
5.7%
s 979
 
3.9%
C 969
 
3.8%
l 933
 
3.7%
Other values (40) 10439
41.3%
Common
ValueCountFrequency (%)
3302
72.2%
. 568
 
12.4%
, 491
 
10.7%
' 49
 
1.1%
& 35
 
0.8%
- 30
 
0.7%
/ 19
 
0.4%
1 19
 
0.4%
5 16
 
0.3%
2 8
 
0.2%
Other values (8) 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3302
 
11.1%
e 2258
 
7.6%
n 1990
 
6.7%
o 1761
 
5.9%
t 1567
 
5.3%
r 1493
 
5.0%
a 1445
 
4.8%
i 1429
 
4.8%
s 979
 
3.3%
C 969
 
3.2%
Other values (58) 12643
42.4%
Distinct159
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size68.8 KiB
2023-12-09T21:04:22.592621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length13.321
Min length8

Characters and Unicode

Total characters13321
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)5.2%

Sample

1st rowAlfred Gordon
2nd rowReid Davis
3rd rowLinda Chiaverini
4th rowJoanne Dowdell
5th rowGilda Ventresca-Ecroyd
ValueCountFrequency (%)
john 59
 
2.9%
glen 35
 
1.7%
bolofsky 35
 
1.7%
o'hare 31
 
1.5%
oglesby 31
 
1.5%
henry 31
 
1.5%
turner 31
 
1.5%
rodney 31
 
1.5%
hussain 29
 
1.4%
raju 29
 
1.4%
Other values (286) 1726
83.5%
2023-12-09T21:04:23.200492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1435
 
10.8%
a 1171
 
8.8%
n 1096
 
8.2%
1068
 
8.0%
r 919
 
6.9%
i 717
 
5.4%
l 623
 
4.7%
o 600
 
4.5%
s 566
 
4.2%
t 390
 
2.9%
Other values (43) 4736
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10058
75.5%
Uppercase Letter 2111
 
15.8%
Space Separator 1068
 
8.0%
Other Punctuation 79
 
0.6%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1435
14.3%
a 1171
11.6%
n 1096
10.9%
r 919
9.1%
i 717
 
7.1%
l 623
 
6.2%
o 600
 
6.0%
s 566
 
5.6%
t 390
 
3.9%
u 363
 
3.6%
Other values (15) 2178
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 194
 
9.2%
B 162
 
7.7%
R 159
 
7.5%
J 158
 
7.5%
A 147
 
7.0%
L 139
 
6.6%
H 128
 
6.1%
S 120
 
5.7%
D 117
 
5.5%
K 106
 
5.0%
Other values (14) 681
32.3%
Other Punctuation
ValueCountFrequency (%)
. 48
60.8%
' 31
39.2%
Space Separator
ValueCountFrequency (%)
1068
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12169
91.4%
Common 1152
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1435
 
11.8%
a 1171
 
9.6%
n 1096
 
9.0%
r 919
 
7.6%
i 717
 
5.9%
l 623
 
5.1%
o 600
 
4.9%
s 566
 
4.7%
t 390
 
3.2%
u 363
 
3.0%
Other values (39) 4289
35.2%
Common
ValueCountFrequency (%)
1068
92.7%
. 48
 
4.2%
' 31
 
2.7%
- 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1435
 
10.8%
a 1171
 
8.8%
n 1096
 
8.2%
1068
 
8.0%
r 919
 
6.9%
i 717
 
5.4%
l 623
 
4.7%
o 600
 
4.5%
s 566
 
4.2%
t 390
 
2.9%
Other values (43) 4736
35.6%
Distinct84
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size133.4 KiB
2023-12-09T21:04:23.585033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length221
Median length169
Mean length79.439
Min length10

Characters and Unicode

Total characters79439
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.3%

Sample

1st rowJonathan Bing; Quinn Caruthers Moreno; Ellen Gustafson; Robert Harding; John Mascialino; Roy Mogilanski; Julia Rogawski; India Sneed; Edward Wallace
2nd rowJonathan Bing; Quinn Caruthers Moreno; Ellen Gustafson; Robert Harding; John Mascialino; Julia Rogawski; India Sneed; Edward Wallace; Roy Mogilanski
3rd rowJonathan Bing; Quinn Caruthers Moreno; Ellen Gustafson; Robert Harding; John Mascialino; Julia Rogawski; India Sneed; Edward Wallace
4th rowJonathan Bing; Quinn Caruthers Moreno; Ellen Gustafson; Robert Harding; John Mascialino; Julia Rogawski; India Sneed; Edward Wallace
5th rowJonathan Bing; Quinn Caruthers Moreno; Ellen Gustafson; Robert Harding; John L Mascialino; Julia Rogawski; India Sneed; Edward Wallace
ValueCountFrequency (%)
allison 392
 
3.7%
katherine 388
 
3.7%
schwab 388
 
3.7%
christ 384
 
3.7%
jenny 384
 
3.7%
fernandez 384
 
3.7%
rose 384
 
3.7%
l 383
 
3.7%
fisher 380
 
3.6%
stuart 379
 
3.6%
Other values (217) 6624
63.3%
2023-12-09T21:04:24.127250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9479
 
11.9%
e 5356
 
6.7%
n 4992
 
6.3%
S 3943
 
5.0%
; 3877
 
4.9%
A 3735
 
4.7%
r 3318
 
4.2%
a 3013
 
3.8%
i 2767
 
3.5%
R 2762
 
3.5%
Other values (44) 36197
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34357
43.2%
Uppercase Letter 31328
39.4%
Space Separator 9479
 
11.9%
Other Punctuation 4263
 
5.4%
Dash Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5356
15.6%
n 4992
14.5%
r 3318
9.7%
a 3013
8.8%
i 2767
8.1%
t 2194
 
6.4%
h 1874
 
5.5%
s 1740
 
5.1%
o 1606
 
4.7%
d 1172
 
3.4%
Other values (15) 6325
18.4%
Uppercase Letter
ValueCountFrequency (%)
S 3943
12.6%
A 3735
11.9%
R 2762
 
8.8%
E 2738
 
8.7%
L 1918
 
6.1%
O 1916
 
6.1%
H 1811
 
5.8%
I 1496
 
4.8%
N 1436
 
4.6%
C 1361
 
4.3%
Other values (14) 8212
26.2%
Other Punctuation
ValueCountFrequency (%)
; 3877
90.9%
. 374
 
8.8%
' 12
 
0.3%
Space Separator
ValueCountFrequency (%)
9479
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65685
82.7%
Common 13754
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5356
 
8.2%
n 4992
 
7.6%
S 3943
 
6.0%
A 3735
 
5.7%
r 3318
 
5.1%
a 3013
 
4.6%
i 2767
 
4.2%
R 2762
 
4.2%
E 2738
 
4.2%
t 2194
 
3.3%
Other values (39) 30867
47.0%
Common
ValueCountFrequency (%)
9479
68.9%
; 3877
28.2%
. 374
 
2.7%
- 12
 
0.1%
' 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9479
 
11.9%
e 5356
 
6.7%
n 4992
 
6.3%
S 3943
 
5.0%
; 3877
 
4.9%
A 3735
 
4.7%
r 3318
 
4.2%
a 3013
 
3.8%
i 2767
 
3.5%
R 2762
 
3.5%
Other values (44) 36197
45.6%
Distinct182
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size144.9 KiB
2023-12-09T21:04:24.518665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length487
Median length235
Mean length91.285
Min length16

Characters and Unicode

Total characters91285
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)6.4%

Sample

1st rowProcurement - Security and data discovery software.
2nd rowProcurement - Technology/Procurement Contracts.
3rd rowLocal Legislation (including introduction) - Bills relating to essential workers financial enhancements
4th rowLocal Legislation (including introduction) - Monitoring of legislation for issues to news paper publication industry
5th rowBudget - Post adoption funding for City First Readers and Adult Literacy.
ValueCountFrequency (%)
1215
 
9.6%
budget 541
 
4.3%
local 538
 
4.3%
legislation 536
 
4.2%
including 494
 
3.9%
introduction 475
 
3.8%
and 339
 
2.7%
of 271
 
2.1%
nyc 259
 
2.1%
issues 251
 
2.0%
Other values (602) 7707
61.0%
2023-12-09T21:04:25.082724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11891
 
13.0%
i 7259
 
8.0%
n 6833
 
7.5%
e 6171
 
6.8%
t 6041
 
6.6%
o 5917
 
6.5%
r 4225
 
4.6%
a 4146
 
4.5%
c 3340
 
3.7%
s 3277
 
3.6%
Other values (63) 32185
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65336
71.6%
Space Separator 11891
 
13.0%
Uppercase Letter 9690
 
10.6%
Dash Punctuation 1372
 
1.5%
Other Punctuation 1266
 
1.4%
Decimal Number 750
 
0.8%
Open Punctuation 490
 
0.5%
Close Punctuation 490
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7259
11.1%
n 6833
10.5%
e 6171
9.4%
t 6041
9.2%
o 5917
 
9.1%
r 4225
 
6.5%
a 4146
 
6.3%
c 3340
 
5.1%
s 3277
 
5.0%
l 3253
 
5.0%
Other values (16) 14874
22.8%
Uppercase Letter
ValueCountFrequency (%)
L 1302
13.4%
C 832
 
8.6%
B 789
 
8.1%
S 677
 
7.0%
T 621
 
6.4%
N 620
 
6.4%
E 562
 
5.8%
I 554
 
5.7%
D 504
 
5.2%
U 402
 
4.1%
Other values (15) 2827
29.2%
Decimal Number
ValueCountFrequency (%)
1 267
35.6%
7 90
 
12.0%
2 89
 
11.9%
6 67
 
8.9%
9 64
 
8.5%
0 53
 
7.1%
8 42
 
5.6%
5 34
 
4.5%
3 26
 
3.5%
4 18
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 567
44.8%
. 401
31.7%
; 253
20.0%
' 24
 
1.9%
/ 14
 
1.1%
: 4
 
0.3%
# 2
 
0.2%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
11891
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1372
100.0%
Open Punctuation
ValueCountFrequency (%)
( 490
100.0%
Close Punctuation
ValueCountFrequency (%)
) 490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75026
82.2%
Common 16259
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7259
 
9.7%
n 6833
 
9.1%
e 6171
 
8.2%
t 6041
 
8.1%
o 5917
 
7.9%
r 4225
 
5.6%
a 4146
 
5.5%
c 3340
 
4.5%
s 3277
 
4.4%
l 3253
 
4.3%
Other values (41) 24564
32.7%
Common
ValueCountFrequency (%)
11891
73.1%
- 1372
 
8.4%
, 567
 
3.5%
( 490
 
3.0%
) 490
 
3.0%
. 401
 
2.5%
1 267
 
1.6%
; 253
 
1.6%
7 90
 
0.6%
2 89
 
0.5%
Other values (12) 349
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11891
 
13.0%
i 7259
 
8.0%
n 6833
 
7.5%
e 6171
 
6.8%
t 6041
 
6.6%
o 5917
 
6.5%
r 4225
 
4.6%
a 4146
 
4.5%
c 3340
 
3.7%
s 3277
 
3.6%
Other values (63) 32185
35.3%

lobbyist_targets
Text

MISSING 

Distinct108
Distinct (%)10.9%
Missing13
Missing (%)1.3%
Memory size2.1 MiB
2023-12-09T21:04:25.523581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length4000
Median length3262
Mean length2226.372847
Min length32

Characters and Unicode

Total characters2197430
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)2.2%

Sample

1st rowCitywide Administrative Services, Department of (DCAS) Unknown Unknown
2nd rowInformation Technology and Telecommunications, Department of (DOITT) Unknown Unknown
3rd rowNYC Council Members Charles Barron - District No. 42; NYC Council Members Gale Brewer - District No. 6; NYC Council Members Margaret Chin - District No. 1; NYC Council Members James Gennaro - District No. 24; NYC Council Members Alan Gerson - District No. 1; NYC Council Members Darlene Mealy - District No. 41; NYC Council Members Shaun Abreu - District No. 7; NYC Council Members Adrienne Adams - District No. 28; NYC Council Members Joann Ariola - District No. 32; NYC Council Members Alexa Aviles - District No. 38; NYC Council Members Diana Ayala - District No. 8; NYC Council Members Joe Borelli - District No. 51; NYC Council Members Erik Bottcher - District No. 3; NYC Council Members Justin Brannan - District No. 43; NYC Council Members Selvena Brooks-Powers - District No. 31; NYC Council Members Tiffany Caban - District No. 22; NYC Council Members David Carr - District No. 50; NYC Council Members Carmen De La Rosa - District No. 10; NYC Council Members Eric Dinowitz - District No. 11; NYC Council Members Amanda Farias - District No. 18; NYC Council Members Oswald Feliz - District No. 15; NYC Council Members Jennifer Gutierrez - District No. 34; NYC Council Members Shahana K. Hanif - District No. 39; NYC Council Members Kamillah Hanks - District No. 49; NYC Council Members Robert Holden - District No. 30; NYC Council Members Crystal Hudson - District No. 35; NYC Council Members Rita C. Joseph - District No. 40; NYC Council Members Ari Kagan - District No. 47; NYC Council Members Shekar Krishnan - District No. 25; NYC Council Members Linda Lee - District No. 23; NYC Council Members Farah Louis - District No. 45; NYC Council Members Christopher Marte - District No. 1; NYC Council Members Julie Menin - District No. 5; NYC Council Members Francisco P. Moya - District No. 21; NYC Council Members Mercedes Narcisse - District No. 46; NYC Council Members Sandy Nurse - District No. 37; NYC Council Members Chi Osse - District No. 36; NYC Council Members Vickie Paladino - District No. 19; NYC Council Members Keith Powers - District No. 4; NYC Council Members Lincoln Restler - District No. 33; NYC Council Members Kristin Richardson Jordan - District No. 9; NYC Council Members Kevin Riley - District No. 12; NYC Council Members Carlina Rivera - District No. 2; NYC Council Members Rafael Salamanca Jr. - District No. 17; NYC Council Members Pierina Ana Sanchez - District No. 14; NYC Council Members Lynn C. Schulman - District No. 29; NYC Council Members Althea Stevens - District No. 16; NYC Council Members Sandra Ung - District No. 20; NYC Council Members Marjorie Velazquez - District No. 13; NYC Council Members Inna Vernikov - District No. 48; NYC Council Members Nantasha M. Williams - District No. 27; NYC Council Members Julie Won - District No. 26; NYC Council Members Kalman Yeger - District No. 44
4th rowMayor, Office of the (OTM) Mayor Unknown Unknown
5th rowNYC Council Members Charles Barron - District No. 42; NYC Council Members Gale Brewer - District No. 6; NYC Council Members Margaret Chin - District No. 1; NYC Council Members James Gennaro - District No. 24; NYC Council Members Alan Gerson - District No. 1; NYC Council Members Darlene Mealy - District No. 41; NYC Council Members Shaun Abreu - District No. 7; NYC Council Members Adrienne Adams - District No. 28; NYC Council Members Joann Ariola - District No. 32; NYC Council Members Alexa Aviles - District No. 38; NYC Council Members Diana Ayala - District No. 8; NYC Council Members Joe Borelli - District No. 51; NYC Council Members Erik Bottcher - District No. 3; NYC Council Members Justin Brannan - District No. 43; NYC Council Members Selvena Brooks-Powers - District No. 31; NYC Council Members Tiffany Caban - District No. 22; NYC Council Members David Carr - District No. 50; NYC Council Members Carmen De La Rosa - District No. 10; NYC Council Members Eric Dinowitz - District No. 11; NYC Council Members Amanda Farias - District No. 18; NYC Council Members Oswald Feliz - District No. 15; NYC Council Members Jennifer Gutierrez - District No. 34; NYC Council Members Shahana K. Hanif - District No. 39; NYC Council Members Kamillah Hanks - District No. 49; NYC Council Members Robert Holden - District No. 30; NYC Council Members Crystal Hudson - District No. 35; NYC Council Members Rita C. Joseph - District No. 40; NYC Council Members Ari Kagan - District No. 47; NYC Council Members Shekar Krishnan - District No. 25; NYC Council Members Linda Lee - District No. 23; NYC Council Members Farah Louis - District No. 45; NYC Council Members Christopher Marte - District No. 1; NYC Council Members Julie Menin - District No. 5; NYC Council Members Francisco P. Moya - District No. 21; NYC Council Members Mercedes Narcisse - District No. 46; NYC Council Members Sandy Nurse - District No. 37; NYC Council Members Chi Osse - District No. 36; NYC Council Members Vickie Paladino - District No. 19; NYC Council Members Keith Powers - District No. 4; NYC Council Members Lincoln Restler - District No. 33; NYC Council Members Kristin Richardson Jordan - District No. 9; NYC Council Members Kevin Riley - District No. 12; NYC Council Members Carlina Rivera - District No. 2; NYC Council Members Rafael Salamanca Jr. - District No. 17; NYC Council Members Pierina Ana Sanchez - District No. 14; NYC Council Members Lynn C. Schulman - District No. 29; NYC Council Members Althea Stevens - District No. 16; NYC Council Members Sandra Ung - District No. 20; NYC Council Members Marjorie Velazquez - District No. 13; NYC Council Members Inna Vernikov - District No. 48; NYC Council Members Nantasha M. Williams - District No. 27; NYC Council Members Julie Won - District No. 26; NYC Council Members Kalman Yeger - District No. 44
ValueCountFrequency (%)
41017
 
11.0%
no 40378
 
10.8%
district 40371
 
10.8%
nyc 39719
 
10.6%
council 39683
 
10.6%
members 39662
 
10.6%
mark 1971
 
0.5%
robert 1858
 
0.5%
rafael 1422
 
0.4%
jr 1410
 
0.4%
Other values (609) 125788
33.7%
2023-12-09T21:04:26.142507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
373285
17.0%
i 153672
 
7.0%
e 136753
 
6.2%
r 125016
 
5.7%
o 117054
 
5.3%
s 102189
 
4.7%
t 98587
 
4.5%
c 90968
 
4.1%
C 89537
 
4.1%
n 89505
 
4.1%
Other values (61) 820864
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1249970
56.9%
Space Separator 373285
 
17.0%
Uppercase Letter 372962
 
17.0%
Other Punctuation 84537
 
3.8%
Decimal Number 73335
 
3.3%
Dash Punctuation 41493
 
1.9%
Open Punctuation 924
 
< 0.1%
Close Punctuation 924
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 153672
12.3%
e 136753
10.9%
r 125016
10.0%
o 117054
9.4%
s 102189
8.2%
t 98587
7.9%
c 90968
7.3%
n 89505
7.2%
l 65828
 
5.3%
a 60233
 
4.8%
Other values (16) 210165
16.8%
Uppercase Letter
ValueCountFrequency (%)
C 89537
24.0%
N 80397
21.6%
M 47964
12.9%
D 47169
12.6%
Y 41745
11.2%
R 10381
 
2.8%
A 7091
 
1.9%
B 6641
 
1.8%
K 5783
 
1.6%
J 5537
 
1.5%
Other values (15) 30717
 
8.2%
Decimal Number
ValueCountFrequency (%)
4 12399
16.9%
3 12294
16.8%
1 11914
16.2%
2 11792
16.1%
5 5204
7.1%
6 4369
 
6.0%
0 4207
 
5.7%
7 3734
 
5.1%
9 3716
 
5.1%
8 3706
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 42809
50.6%
; 40107
47.4%
, 1501
 
1.8%
' 63
 
0.1%
& 32
 
< 0.1%
/ 25
 
< 0.1%
Space Separator
ValueCountFrequency (%)
373285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41493
100.0%
Open Punctuation
ValueCountFrequency (%)
( 924
100.0%
Close Punctuation
ValueCountFrequency (%)
) 924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1622932
73.9%
Common 574498
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 153672
 
9.5%
e 136753
 
8.4%
r 125016
 
7.7%
o 117054
 
7.2%
s 102189
 
6.3%
t 98587
 
6.1%
c 90968
 
5.6%
C 89537
 
5.5%
n 89505
 
5.5%
N 80397
 
5.0%
Other values (41) 539254
33.2%
Common
ValueCountFrequency (%)
373285
65.0%
. 42809
 
7.5%
- 41493
 
7.2%
; 40107
 
7.0%
4 12399
 
2.2%
3 12294
 
2.1%
1 11914
 
2.1%
2 11792
 
2.1%
5 5204
 
0.9%
6 4369
 
0.8%
Other values (10) 18832
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2197430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
373285
17.0%
i 153672
 
7.0%
e 136753
 
6.2%
r 125016
 
5.7%
o 117054
 
5.3%
s 102189
 
4.7%
t 98587
 
4.5%
c 90968
 
4.1%
C 89537
 
4.1%
n 89505
 
4.1%
Other values (61) 820864
37.4%

lobbyist_is_colobbyist
Text

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-09T21:04:26.257482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1000
100.0%
2023-12-09T21:04:26.505579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%
Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size89.2 KiB
2023-12-09T21:04:26.725900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length56
Median length44
Mean length34.225
Min length3

Characters and Unicode

Total characters34225
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechnology, Telecommunications & Other Media
2nd rowTechnology, Telecommunications & Other Media
3rd rowTrade Associations
4th rowTechnology, Telecommunications & Other Media
5th rowHealth & Mental Hygiene
ValueCountFrequency (%)
734
18.5%
public 273
 
6.9%
not-for-profit 273
 
6.9%
organization 273
 
6.9%
community 273
 
6.9%
interest 273
 
6.9%
telecommunications 127
 
3.2%
other 127
 
3.2%
media 127
 
3.2%
technology 127
 
3.2%
Other values (35) 1371
34.5%
2023-12-09T21:04:27.111187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2978
 
8.7%
i 2903
 
8.5%
t 2879
 
8.4%
n 2854
 
8.3%
o 2600
 
7.6%
e 2290
 
6.7%
a 1968
 
5.8%
r 1964
 
5.7%
s 1085
 
3.2%
c 1019
 
3.0%
Other values (33) 11685
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26152
76.4%
Uppercase Letter 3244
 
9.5%
Space Separator 2978
 
8.7%
Other Punctuation 1305
 
3.8%
Dash Punctuation 546
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2903
11.1%
t 2879
11.0%
n 2854
10.9%
o 2600
9.9%
e 2290
8.8%
a 1968
 
7.5%
r 1964
 
7.5%
s 1085
 
4.1%
c 1019
 
3.9%
l 989
 
3.8%
Other values (13) 5601
21.4%
Uppercase Letter
ValueCountFrequency (%)
T 424
13.1%
O 400
12.3%
C 377
11.6%
N 285
8.8%
P 279
8.6%
I 273
8.4%
M 268
8.3%
E 190
 
5.9%
R 130
 
4.0%
F 128
 
3.9%
Other values (6) 490
15.1%
Other Punctuation
ValueCountFrequency (%)
& 734
56.2%
, 571
43.8%
Space Separator
ValueCountFrequency (%)
2978
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29396
85.9%
Common 4829
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2903
 
9.9%
t 2879
 
9.8%
n 2854
 
9.7%
o 2600
 
8.8%
e 2290
 
7.8%
a 1968
 
6.7%
r 1964
 
6.7%
s 1085
 
3.7%
c 1019
 
3.5%
l 989
 
3.4%
Other values (29) 8845
30.1%
Common
ValueCountFrequency (%)
2978
61.7%
& 734
 
15.2%
, 571
 
11.8%
- 546
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2978
 
8.7%
i 2903
 
8.5%
t 2879
 
8.4%
n 2854
 
8.3%
o 2600
 
7.6%
e 2290
 
6.7%
a 1968
 
5.8%
r 1964
 
5.7%
s 1085
 
3.2%
c 1019
 
3.0%
Other values (33) 11685
34.1%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-09T21:04:27.230594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1000
100.0%
2023-12-09T21:04:27.460132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%

num_periods
Text

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-09T21:04:27.567835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6
ValueCountFrequency (%)
6 1000
100.0%
2023-12-09T21:04:27.782152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1000
100.0%
Distinct54
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-12-09T21:04:28.063451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23000
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row2023-01-01T00:00:00.000
2nd row2023-01-01T00:00:00.000
3rd row2023-01-01T00:00:00.000
4th row2023-01-01T00:00:00.000
5th row2023-01-01T00:00:00.000
ValueCountFrequency (%)
2018-01-01t00:00:00.000 243
24.3%
2019-01-01t00:00:00.000 206
20.6%
2020-01-01t00:00:00.000 90
 
9.0%
2022-01-01t00:00:00.000 75
 
7.5%
2021-01-01t00:00:00.000 66
 
6.6%
2023-01-01t00:00:00.000 64
 
6.4%
2018-01-29t00:00:00.000 24
 
2.4%
2019-02-01t00:00:00.000 12
 
1.2%
2018-01-05t00:00:00.000 12
 
1.2%
2019-04-01t00:00:00.000 8
 
0.8%
Other values (44) 200
20.0%
2023-12-09T21:04:28.471498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12012
52.2%
1 2454
 
10.7%
- 2000
 
8.7%
: 2000
 
8.7%
2 1626
 
7.1%
T 1000
 
4.3%
. 1000
 
4.3%
8 370
 
1.6%
9 309
 
1.3%
3 101
 
0.4%
Other values (4) 128
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17000
73.9%
Other Punctuation 3000
 
13.0%
Dash Punctuation 2000
 
8.7%
Uppercase Letter 1000
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12012
70.7%
1 2454
 
14.4%
2 1626
 
9.6%
8 370
 
2.2%
9 309
 
1.8%
3 101
 
0.6%
5 38
 
0.2%
4 36
 
0.2%
6 30
 
0.2%
7 24
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 2000
66.7%
. 1000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22000
95.7%
Latin 1000
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12012
54.6%
1 2454
 
11.2%
- 2000
 
9.1%
: 2000
 
9.1%
2 1626
 
7.4%
. 1000
 
4.5%
8 370
 
1.7%
9 309
 
1.4%
3 101
 
0.5%
5 38
 
0.2%
Other values (3) 90
 
0.4%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12012
52.2%
1 2454
 
10.7%
- 2000
 
8.7%
: 2000
 
8.7%
2 1626
 
7.1%
T 1000
 
4.3%
. 1000
 
4.3%
8 370
 
1.6%
9 309
 
1.3%
3 101
 
0.4%
Other values (4) 128
 
0.6%
Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-12-09T21:04:28.711067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23000
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.7%

Sample

1st row2023-12-31T00:00:00.000
2nd row2023-12-31T00:00:00.000
3rd row2023-12-31T00:00:00.000
4th row2023-12-31T00:00:00.000
5th row2023-12-31T00:00:00.000
ValueCountFrequency (%)
2018-12-31t00:00:00.000 319
31.9%
2019-12-31t00:00:00.000 255
25.5%
2020-12-31t00:00:00.000 124
 
12.4%
2022-12-31t00:00:00.000 104
 
10.4%
2021-12-31t00:00:00.000 84
 
8.4%
2023-12-31t00:00:00.000 55
 
5.5%
2019-06-30t00:00:00.000 7
 
0.7%
2018-12-15t00:00:00.000 6
 
0.6%
2018-12-09t00:00:00.000 6
 
0.6%
2018-07-17t00:00:00.000 5
 
0.5%
Other values (16) 35
 
3.5%
2023-12-09T21:04:29.050681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10201
44.4%
1 2629
 
11.4%
2 2442
 
10.6%
- 2000
 
8.7%
: 2000
 
8.7%
3 1047
 
4.6%
T 1000
 
4.3%
. 1000
 
4.3%
8 354
 
1.5%
9 283
 
1.2%
Other values (4) 44
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17000
73.9%
Other Punctuation 3000
 
13.0%
Dash Punctuation 2000
 
8.7%
Uppercase Letter 1000
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10201
60.0%
1 2629
 
15.5%
2 2442
 
14.4%
3 1047
 
6.2%
8 354
 
2.1%
9 283
 
1.7%
6 17
 
0.1%
5 14
 
0.1%
7 10
 
0.1%
4 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 2000
66.7%
. 1000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22000
95.7%
Latin 1000
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10201
46.4%
1 2629
 
11.9%
2 2442
 
11.1%
- 2000
 
9.1%
: 2000
 
9.1%
3 1047
 
4.8%
. 1000
 
4.5%
8 354
 
1.6%
9 283
 
1.3%
6 17
 
0.1%
Other values (3) 27
 
0.1%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10201
44.4%
1 2629
 
11.4%
2 2442
 
10.6%
- 2000
 
8.7%
: 2000
 
8.7%
3 1047
 
4.6%
T 1000
 
4.3%
. 1000
 
4.3%
8 354
 
1.5%
9 283
 
1.2%
Other values (4) 44
 
0.2%

periodic_id
Text

MISSING 

Distinct933
Distinct (%)100.0%
Missing67
Missing (%)6.7%
Memory size59.6 KiB
2023-12-09T21:04:29.479056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5598
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique933 ?
Unique (%)100.0%

Sample

1st row462138
2nd row466815
3rd row470493
4th row479107
5th row453331
ValueCountFrequency (%)
381042 1
 
0.1%
395881 1
 
0.1%
371177 1
 
0.1%
350776 1
 
0.1%
424544 1
 
0.1%
386240 1
 
0.1%
390130 1
 
0.1%
503382 1
 
0.1%
382548 1
 
0.1%
513447 1
 
0.1%
Other values (923) 923
98.9%
2023-12-09T21:04:30.034258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 900
16.1%
4 812
14.5%
5 691
12.3%
0 578
10.3%
6 506
9.0%
1 482
8.6%
7 438
7.8%
9 431
7.7%
8 380
6.8%
2 380
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 900
16.1%
4 812
14.5%
5 691
12.3%
0 578
10.3%
6 506
9.0%
1 482
8.6%
7 438
7.8%
9 431
7.7%
8 380
6.8%
2 380
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 900
16.1%
4 812
14.5%
5 691
12.3%
0 578
10.3%
6 506
9.0%
1 482
8.6%
7 438
7.8%
9 431
7.7%
8 380
6.8%
2 380
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 900
16.1%
4 812
14.5%
5 691
12.3%
0 578
10.3%
6 506
9.0%
1 482
8.6%
7 438
7.8%
9 431
7.7%
8 380
6.8%
2 380
6.8%

registration_id
Text

MISSING 

Distinct171
Distinct (%)18.3%
Missing67
Missing (%)6.7%
Memory size59.6 KiB
2023-12-09T21:04:30.478338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5598
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row461704
2nd row461704
3rd row461704
4th row461704
5th row461704
ValueCountFrequency (%)
363307 6
 
0.6%
459897 6
 
0.6%
340257 6
 
0.6%
363296 6
 
0.6%
385269 6
 
0.6%
413320 6
 
0.6%
363329 6
 
0.6%
432911 6
 
0.6%
363300 6
 
0.6%
452356 6
 
0.6%
Other values (161) 873
93.6%
2023-12-09T21:04:31.043813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1324
23.7%
4 665
11.9%
9 593
10.6%
6 510
 
9.1%
1 500
 
8.9%
2 448
 
8.0%
7 403
 
7.2%
8 401
 
7.2%
0 377
 
6.7%
5 377
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1324
23.7%
4 665
11.9%
9 593
10.6%
6 510
 
9.1%
1 500
 
8.9%
2 448
 
8.0%
7 403
 
7.2%
8 401
 
7.2%
0 377
 
6.7%
5 377
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1324
23.7%
4 665
11.9%
9 593
10.6%
6 510
 
9.1%
1 500
 
8.9%
2 448
 
8.0%
7 403
 
7.2%
8 401
 
7.2%
0 377
 
6.7%
5 377
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1324
23.7%
4 665
11.9%
9 593
10.6%
6 510
 
9.1%
1 500
 
8.9%
2 448
 
8.0%
7 403
 
7.2%
8 401
 
7.2%
0 377
 
6.7%
5 377
 
6.7%

period
Text

MISSING 

Distinct6
Distinct (%)0.6%
Missing67
Missing (%)6.7%
Memory size55.1 KiB
2023-12-09T21:04:31.232209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters933
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row5
4th row6
5th row1
ValueCountFrequency (%)
6 171
18.3%
5 160
17.1%
4 157
16.8%
3 154
16.5%
2 149
16.0%
1 142
15.2%
2023-12-09T21:04:31.517011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 171
18.3%
5 160
17.1%
4 157
16.8%
3 154
16.5%
2 149
16.0%
1 142
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 933
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 171
18.3%
5 160
17.1%
4 157
16.8%
3 154
16.5%
2 149
16.0%
1 142
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 933
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 171
18.3%
5 160
17.1%
4 157
16.8%
3 154
16.5%
2 149
16.0%
1 142
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 171
18.3%
5 160
17.1%
4 157
16.8%
3 154
16.5%
2 149
16.0%
1 142
15.2%

periodic_year
Text

MISSING 

Distinct5
Distinct (%)0.5%
Missing67
Missing (%)6.7%
Memory size57.8 KiB
2023-12-09T21:04:31.679024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3732
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021
ValueCountFrequency (%)
2018 346
37.1%
2019 272
29.2%
2020 124
 
13.3%
2022 107
 
11.5%
2021 84
 
9.0%
2023-12-09T21:04:31.955077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1355
36.3%
0 1057
28.3%
1 702
18.8%
8 346
 
9.3%
9 272
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3732
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1355
36.3%
0 1057
28.3%
1 702
18.8%
8 346
 
9.3%
9 272
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1355
36.3%
0 1057
28.3%
1 702
18.8%
8 346
 
9.3%
9 272
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1355
36.3%
0 1057
28.3%
1 702
18.8%
8 346
 
9.3%
9 272
 
7.3%

compensation_total
Text

MISSING 

Distinct173
Distinct (%)18.5%
Missing67
Missing (%)6.7%
Memory size57.5 KiB
2023-12-09T21:04:32.377251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.723472669
Min length1

Characters and Unicode

Total characters3474
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)13.9%

Sample

1st row20000
2nd row20000
3rd row20000
4th row20000
5th row20000
ValueCountFrequency (%)
0 205
22.0%
10000 89
 
9.5%
2000 52
 
5.6%
20000 51
 
5.5%
8000 48
 
5.1%
6000 43
 
4.6%
18000 31
 
3.3%
12000 29
 
3.1%
5000 27
 
2.9%
15000 23
 
2.5%
Other values (163) 335
35.9%
2023-12-09T21:04:32.932041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2079
59.8%
1 360
 
10.4%
2 228
 
6.6%
5 171
 
4.9%
8 146
 
4.2%
6 115
 
3.3%
4 104
 
3.0%
3 84
 
2.4%
7 71
 
2.0%
. 61
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3413
98.2%
Other Punctuation 61
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2079
60.9%
1 360
 
10.5%
2 228
 
6.7%
5 171
 
5.0%
8 146
 
4.3%
6 115
 
3.4%
4 104
 
3.0%
3 84
 
2.5%
7 71
 
2.1%
9 55
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2079
59.8%
1 360
 
10.4%
2 228
 
6.6%
5 171
 
4.9%
8 146
 
4.2%
6 115
 
3.3%
4 104
 
3.0%
3 84
 
2.4%
7 71
 
2.0%
. 61
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2079
59.8%
1 360
 
10.4%
2 228
 
6.6%
5 171
 
4.9%
8 146
 
4.2%
6 115
 
3.3%
4 104
 
3.0%
3 84
 
2.4%
7 71
 
2.0%
. 61
 
1.8%
Distinct51
Distinct (%)5.5%
Missing67
Missing (%)6.7%
Memory size55.3 KiB
2023-12-09T21:04:33.169177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.213290461
Min length1

Characters and Unicode

Total characters1132
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)5.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 877
94.0%
85 6
 
0.6%
570.96 2
 
0.2%
4010.86 1
 
0.1%
450 1
 
0.1%
1329.95 1
 
0.1%
5182.88 1
 
0.1%
56.35 1
 
0.1%
224 1
 
0.1%
270.2 1
 
0.1%
Other values (41) 41
 
4.4%
2023-12-09T21:04:33.532962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 893
78.9%
5 34
 
3.0%
8 32
 
2.8%
. 27
 
2.4%
2 26
 
2.3%
9 24
 
2.1%
3 24
 
2.1%
1 23
 
2.0%
4 20
 
1.8%
6 16
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1105
97.6%
Other Punctuation 27
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 893
80.8%
5 34
 
3.1%
8 32
 
2.9%
2 26
 
2.4%
9 24
 
2.2%
3 24
 
2.2%
1 23
 
2.1%
4 20
 
1.8%
6 16
 
1.4%
7 13
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 893
78.9%
5 34
 
3.0%
8 32
 
2.8%
. 27
 
2.4%
2 26
 
2.3%
9 24
 
2.1%
3 24
 
2.1%
1 23
 
2.0%
4 20
 
1.8%
6 16
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 893
78.9%
5 34
 
3.0%
8 32
 
2.8%
. 27
 
2.4%
2 26
 
2.3%
9 24
 
2.1%
3 24
 
2.1%
1 23
 
2.0%
4 20
 
1.8%
6 16
 
1.4%

small_expense_total
Text

MISSING 

Distinct16
Distinct (%)1.7%
Missing67
Missing (%)6.7%
Memory size55.1 KiB
2023-12-09T21:04:33.693339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.025723473
Min length1

Characters and Unicode

Total characters957
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 917
98.3%
25 2
 
0.2%
47 1
 
0.1%
72 1
 
0.1%
170 1
 
0.1%
143 1
 
0.1%
14.64 1
 
0.1%
57 1
 
0.1%
96 1
 
0.1%
190 1
 
0.1%
Other values (6) 6
 
0.6%
2023-12-09T21:04:33.972524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 919
96.0%
5 6
 
0.6%
4 6
 
0.6%
2 5
 
0.5%
1 5
 
0.5%
7 4
 
0.4%
6 3
 
0.3%
8 3
 
0.3%
3 2
 
0.2%
. 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 955
99.8%
Other Punctuation 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 919
96.2%
5 6
 
0.6%
4 6
 
0.6%
2 5
 
0.5%
1 5
 
0.5%
7 4
 
0.4%
6 3
 
0.3%
8 3
 
0.3%
3 2
 
0.2%
9 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 957
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 919
96.0%
5 6
 
0.6%
4 6
 
0.6%
2 5
 
0.5%
1 5
 
0.5%
7 4
 
0.4%
6 3
 
0.3%
8 3
 
0.3%
3 2
 
0.2%
. 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 919
96.0%
5 6
 
0.6%
4 6
 
0.6%
2 5
 
0.5%
1 5
 
0.5%
7 4
 
0.4%
6 3
 
0.3%
8 3
 
0.3%
3 2
 
0.2%
. 2
 
0.2%
Distinct27
Distinct (%)2.9%
Missing67
Missing (%)6.7%
Memory size55.2 KiB
2023-12-09T21:04:34.175761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.140407288
Min length1

Characters and Unicode

Total characters1064
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)2.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 905
97.0%
856.44 2
 
0.2%
570.96 2
 
0.2%
5158.5 1
 
0.1%
24542.91 1
 
0.1%
1300 1
 
0.1%
3948 1
 
0.1%
1377.55 1
 
0.1%
12176.63 1
 
0.1%
1998.36 1
 
0.1%
Other values (17) 17
 
1.8%
2023-12-09T21:04:34.507416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 914
85.9%
5 19
 
1.8%
. 19
 
1.8%
4 16
 
1.5%
1 16
 
1.5%
2 16
 
1.5%
6 15
 
1.4%
9 14
 
1.3%
3 13
 
1.2%
8 12
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1045
98.2%
Other Punctuation 19
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 914
87.5%
5 19
 
1.8%
4 16
 
1.5%
1 16
 
1.5%
2 16
 
1.5%
6 15
 
1.4%
9 14
 
1.3%
3 13
 
1.2%
8 12
 
1.1%
7 10
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 914
85.9%
5 19
 
1.8%
. 19
 
1.8%
4 16
 
1.5%
1 16
 
1.5%
2 16
 
1.5%
6 15
 
1.4%
9 14
 
1.3%
3 13
 
1.2%
8 12
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 914
85.9%
5 19
 
1.8%
. 19
 
1.8%
4 16
 
1.5%
1 16
 
1.5%
2 16
 
1.5%
6 15
 
1.4%
9 14
 
1.3%
3 13
 
1.2%
8 12
 
1.1%

salary_expense_total
Text

MISSING 

Distinct34
Distinct (%)3.6%
Missing67
Missing (%)6.7%
Memory size55.2 KiB
2023-12-09T21:04:34.704663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.123258307
Min length1

Characters and Unicode

Total characters1048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)3.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 895
95.9%
85 6
 
0.6%
84 1
 
0.1%
450 1
 
0.1%
125.82 1
 
0.1%
442 1
 
0.1%
1733.68 1
 
0.1%
2044.83 1
 
0.1%
510 1
 
0.1%
2267.65 1
 
0.1%
Other values (24) 24
 
2.6%
2023-12-09T21:04:35.023012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 909
86.7%
8 23
 
2.2%
1 21
 
2.0%
. 17
 
1.6%
5 16
 
1.5%
6 14
 
1.3%
2 13
 
1.2%
3 11
 
1.0%
9 10
 
1.0%
7 8
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1031
98.4%
Other Punctuation 17
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 909
88.2%
8 23
 
2.2%
1 21
 
2.0%
5 16
 
1.6%
6 14
 
1.4%
2 13
 
1.3%
3 11
 
1.1%
9 10
 
1.0%
7 8
 
0.8%
4 6
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 909
86.7%
8 23
 
2.2%
1 21
 
2.0%
. 17
 
1.6%
5 16
 
1.5%
6 14
 
1.3%
2 13
 
1.2%
3 11
 
1.0%
9 10
 
1.0%
7 8
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 909
86.7%
8 23
 
2.2%
1 21
 
2.0%
. 17
 
1.6%
5 16
 
1.5%
6 14
 
1.3%
2 13
 
1.2%
3 11
 
1.0%
9 10
 
1.0%
7 8
 
0.8%

reimbursed_expenses_total
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing67
Missing (%)6.7%
Memory size55.1 KiB
2023-12-09T21:04:35.141407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters933
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 933
100.0%
2023-12-09T21:04:35.354808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 933
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 933
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 933
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 933
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 933
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 933
100.0%

periodic_activities
Text

MISSING 

Distinct385
Distinct (%)70.8%
Missing456
Missing (%)45.6%
Memory size102.0 KiB
2023-12-09T21:04:35.720901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1538
Median length314
Mean length107.8125
Min length15

Characters and Unicode

Total characters58650
Distinct characters80
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)65.4%

Sample

1st rowLocal Legislation (including introduction) - Intro 2289, Intro 2296
2nd rowLocal Legislation (including introduction) - Intro 2311-2021; Intro 2298-2021; Intro 2294-2021; Intro 1897-2020
3rd rowLocal Legislation (including introduction) - Intro 2298-2021; Intro 1775
4th rowLocal Legislation (including introduction) - Intro 1775
5th rowLocal Legislation (including introduction) - ecommerce
ValueCountFrequency (%)
818
 
10.1%
local 321
 
4.0%
budget 307
 
3.8%
including 298
 
3.7%
legislation 294
 
3.6%
introduction 287
 
3.5%
and 218
 
2.7%
intro 179
 
2.2%
of 161
 
2.0%
regarding 150
 
1.9%
Other values (1069) 5061
62.5%
2023-12-09T21:04:36.292481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7657
 
13.1%
n 4412
 
7.5%
i 4291
 
7.3%
e 3724
 
6.3%
t 3634
 
6.2%
o 3542
 
6.0%
r 2809
 
4.8%
a 2574
 
4.4%
s 2187
 
3.7%
d 1780
 
3.0%
Other values (70) 22040
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39567
67.5%
Space Separator 7657
 
13.1%
Uppercase Letter 7617
 
13.0%
Decimal Number 1548
 
2.6%
Dash Punctuation 963
 
1.6%
Other Punctuation 710
 
1.2%
Open Punctuation 293
 
0.5%
Close Punctuation 293
 
0.5%
Math Symbol 1
 
< 0.1%
Currency Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4412
11.2%
i 4291
10.8%
e 3724
9.4%
t 3634
9.2%
o 3542
 
9.0%
r 2809
 
7.1%
a 2574
 
6.5%
s 2187
 
5.5%
d 1780
 
4.5%
l 1756
 
4.4%
Other values (17) 8858
22.4%
Uppercase Letter
ValueCountFrequency (%)
L 812
 
10.7%
I 637
 
8.4%
E 537
 
7.1%
C 501
 
6.6%
S 484
 
6.4%
D 481
 
6.3%
N 473
 
6.2%
T 448
 
5.9%
B 430
 
5.6%
R 417
 
5.5%
Other values (16) 2397
31.5%
Other Punctuation
ValueCountFrequency (%)
, 437
61.5%
. 116
 
16.3%
; 58
 
8.2%
' 31
 
4.4%
: 31
 
4.4%
/ 18
 
2.5%
& 11
 
1.5%
" 4
 
0.6%
? 2
 
0.3%
# 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 414
26.7%
2 320
20.7%
0 181
11.7%
7 147
 
9.5%
9 124
 
8.0%
6 106
 
6.8%
8 91
 
5.9%
4 71
 
4.6%
3 58
 
3.7%
5 36
 
2.3%
Space Separator
ValueCountFrequency (%)
7657
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 963
100.0%
Open Punctuation
ValueCountFrequency (%)
( 293
100.0%
Close Punctuation
ValueCountFrequency (%)
) 293
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47184
80.5%
Common 11466
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4412
 
9.4%
i 4291
 
9.1%
e 3724
 
7.9%
t 3634
 
7.7%
o 3542
 
7.5%
r 2809
 
6.0%
a 2574
 
5.5%
s 2187
 
4.6%
d 1780
 
3.8%
l 1756
 
3.7%
Other values (43) 16475
34.9%
Common
ValueCountFrequency (%)
7657
66.8%
- 963
 
8.4%
, 437
 
3.8%
1 414
 
3.6%
2 320
 
2.8%
( 293
 
2.6%
) 293
 
2.6%
0 181
 
1.6%
7 147
 
1.3%
9 124
 
1.1%
Other values (17) 637
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58649
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7657
 
13.1%
n 4412
 
7.5%
i 4291
 
7.3%
e 3724
 
6.3%
t 3634
 
6.2%
o 3542
 
6.0%
r 2809
 
4.8%
a 2574
 
4.4%
s 2187
 
3.7%
d 1780
 
3.0%
Other values (69) 22039
37.6%
None
ValueCountFrequency (%)
â 1
100.0%

periodic_targets
Text

MISSING 

Distinct491
Distinct (%)90.4%
Missing457
Missing (%)45.7%
Memory size316.5 KiB
2023-12-09T21:04:36.665093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length4000
Median length687
Mean length512.6574586
Min length33

Characters and Unicode

Total characters278373
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique470 ?
Unique (%)86.6%

Sample

1st rowNYC Council Members Keith Powers - District No. 4; NYC Council Members Carlina Rivera - District No. 2
2nd rowNYC Council Members Diana Ayala - District No. 8; NYC Council Members Francisco P. Moya - District No. 21
3rd rowNYC Council Members Diana Ayala - District No. 8; NYC Council Members Francisco P. Moya - District No. 21
4th rowNYC Council Members James Van Bramer - District No. 26
5th rowNYC Council Members Mark Levine - District No. 7
ValueCountFrequency (%)
5129
 
11.1%
nyc 4107
 
8.9%
council 4061
 
8.8%
no 3735
 
8.1%
district 3707
 
8.0%
members 3694
 
8.0%
staff 1221
 
2.6%
of 583
 
1.3%
committee 378
 
0.8%
office 308
 
0.7%
Other values (1236) 19336
41.8%
2023-12-09T21:04:37.227854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46870
16.8%
i 17138
 
6.2%
e 16811
 
6.0%
o 14640
 
5.3%
r 14110
 
5.1%
t 13316
 
4.8%
n 11444
 
4.1%
s 10784
 
3.9%
C 10594
 
3.8%
a 9636
 
3.5%
Other values (63) 113030
40.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 155234
55.8%
Uppercase Letter 53185
 
19.1%
Space Separator 46870
 
16.8%
Other Punctuation 10296
 
3.7%
Decimal Number 6562
 
2.4%
Dash Punctuation 5223
 
1.9%
Open Punctuation 501
 
0.2%
Close Punctuation 501
 
0.2%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 17138
11.0%
e 16811
10.8%
o 14640
9.4%
r 14110
9.1%
t 13316
8.6%
n 11444
 
7.4%
s 10784
 
6.9%
a 9636
 
6.2%
c 9561
 
6.2%
l 7558
 
4.9%
Other values (16) 30236
19.5%
Uppercase Letter
ValueCountFrequency (%)
C 10594
19.9%
N 8739
16.4%
D 5416
10.2%
M 5391
10.1%
Y 4495
8.5%
S 2457
 
4.6%
R 1840
 
3.5%
A 1801
 
3.4%
E 1507
 
2.8%
B 1285
 
2.4%
Other values (16) 9660
18.2%
Decimal Number
ValueCountFrequency (%)
1 1172
17.9%
3 1148
17.5%
2 1068
16.3%
4 1029
15.7%
5 477
7.3%
6 367
 
5.6%
7 361
 
5.5%
0 331
 
5.0%
8 330
 
5.0%
9 279
 
4.3%
Other Punctuation
ValueCountFrequency (%)
; 4413
42.9%
. 3935
38.2%
, 1834
17.8%
' 76
 
0.7%
& 34
 
0.3%
/ 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
46870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5223
100.0%
Open Punctuation
ValueCountFrequency (%)
( 501
100.0%
Close Punctuation
ValueCountFrequency (%)
) 501
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 208419
74.9%
Common 69954
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 17138
 
8.2%
e 16811
 
8.1%
o 14640
 
7.0%
r 14110
 
6.8%
t 13316
 
6.4%
n 11444
 
5.5%
s 10784
 
5.2%
C 10594
 
5.1%
a 9636
 
4.6%
c 9561
 
4.6%
Other values (42) 80385
38.6%
Common
ValueCountFrequency (%)
46870
67.0%
- 5223
 
7.5%
; 4413
 
6.3%
. 3935
 
5.6%
, 1834
 
2.6%
1 1172
 
1.7%
3 1148
 
1.6%
2 1068
 
1.5%
4 1029
 
1.5%
( 501
 
0.7%
Other values (11) 2761
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46870
16.8%
i 17138
 
6.2%
e 16811
 
6.0%
o 14640
 
5.3%
r 14110
 
5.1%
t 13316
 
4.8%
n 11444
 
4.1%
s 10784
 
3.9%
C 10594
 
3.8%
a 9636
 
3.5%
Other values (63) 113030
40.6%